Improving Electrical Demand Baseline Forecasting Techniques

Loads on the electricity grid are increasingly being viewed as potential controllable assets, rather than just a traditional load that generators must supply. This evolving view of loads in this manner has spurred the development of demand response methods for controlling the loads in real time in response to market price and regulation signals. In order to properly compensate the participants in demand response, as well as to better control the loads in aggregate, methods are required to estimate what the electrical load would have been in absence of being called on to temporarily curtail their load had no demand response been called on. This project will apply advanced statistical modeling methods to the problem of computing baseline estimates of load. These methods can provide better predictions that include estimates of uncertainty and can be updated in real time as more data becomes available. Enbala Power Networks will be able to implement these methods in the commercial demand response platform to improve their operations in various demand response applications.

Faculty Supervisor:

Curran Crawford

Student:

Partner:

Enbala Power Networks Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Utilities

University:

University of Victoria

Program: